On-line Learning of Unknown Hand Held Objects via Tracking

Proc. Second International Cognitive Vision Workshop

For many computer vision applications labeled/segmented data is needed. Manually
assigning labels or segmenting images is a time consuming and tedious task and
becomes infeasible for a huge amount of data (e.g., when analyzing a video
stream). Thus, this paper proposes a new approach to minimize the manual
labeling/segmentation effort for learning an object detector by automatically
extracting training data directly from a video sequence. Therefore, a robust
background model, a tracker and an on-line learning method are combined. The
main idea is to track an object through a video sequence and to directly use the
obtained image patches, showing the object from different views, to
incrementally update an existing model which in turn can be used for detection.
As the tracker is initialized automatically by change detection, no user
interaction is needed! Thus, an unknown object can be learned without having
any prior information. To show the benefit of the proposed approach the
framework is demonstrated on several typical objects that can be found on a
desktop.

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